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Seoul National UniversityDepartment of Electrical and Computer Engineering
Ph.D. Student (Advisor: Yunheung Paek)Mar. 2022 - present -
Ewha Womans UniversityB.S. in Cyber Security (Cum Laude)Mar. 2018 - Feb. 2022
Honors & Awards
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Excellence Paper Award of ACK 2024 (granted by KIPS)2024
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Excellence Paper Award of ASK 2023 (granted by KIPS)2023
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Academic Excellence Scholarship (granted by Ewha Womans University)2020-2022
News
Selected Publications (view all )
SLOTHE : Lazy Approximation of Non-Arithmetic Neural Network Functions over Encrypted Data
Kevin Nam*, Youyeon Joo*, Seungjin Ha, Yunheung Paek$\dagger$ (* equal contribution, $\dagger$ corresponding author)
USENIX Security Symposium (USENIX Sec), 2025
Existing works adopt an eager approximation (EA) strategy to approximate non-arithmetic functions (NAFs), which statically replaces each NAF with a fixed polynomial, locking in computational errors and limiting optimization opportunities. We propose SLOTHE, a lazy approximation (LA) solution that recursively decomposes NAF codes into arithmetic and nonarithmetic sub-functions, selectively approximating only the non-arithmetic components when required.
SLOTHE : Lazy Approximation of Non-Arithmetic Neural Network Functions over Encrypted Data
Kevin Nam*, Youyeon Joo*, Seungjin Ha, Yunheung Paek$\dagger$ (* equal contribution, $\dagger$ corresponding author)
2025
Existing works adopt an eager approximation (EA) strategy to approximate non-arithmetic functions (NAFs), which statically replaces each NAF with a fixed polynomial, locking in computational errors and limiting optimization opportunities. We propose SLOTHE, a lazy approximation (LA) solution that recursively decomposes NAF codes into arithmetic and nonarithmetic sub-functions, selectively approximating only the non-arithmetic components when required.
LOHEN: Layer-wise Optimizations for Neural Network Inferences over Encrypted Data with High Performance or Accuracy
Kevin Nam, Youyeon Joo, Dongju Lee, Seungjin Ha, Hyunyoung Oh, Hyungon Moon$\dagger$, Yunheung Paek$\dagger$ ($\dagger$ corresponding author)
USENIX Security Symposium (USENIX Sec), 2025
When FHE is applied to neural networks (NNs), we have observed that the distinct layered architecture of NN models opens the door for a performance improvement by using layer-wise Ciphertext Configurations (CCs), because a globally chosen CC may not be the best possible CC for every layer individually. This paper introduces LOHEN, a technique crafted to attain high performance of NN inference by enabling to use layer-wise CC efficiently.
LOHEN: Layer-wise Optimizations for Neural Network Inferences over Encrypted Data with High Performance or Accuracy
Kevin Nam, Youyeon Joo, Dongju Lee, Seungjin Ha, Hyunyoung Oh, Hyungon Moon$\dagger$, Yunheung Paek$\dagger$ ($\dagger$ corresponding author)
2025
When FHE is applied to neural networks (NNs), we have observed that the distinct layered architecture of NN models opens the door for a performance improvement by using layer-wise Ciphertext Configurations (CCs), because a globally chosen CC may not be the best possible CC for every layer individually. This paper introduces LOHEN, a technique crafted to attain high performance of NN inference by enabling to use layer-wise CC efficiently.